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-rw-r--r--src/ipa/raspberrypi/controller/rpi/awb.cpp734
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diff --git a/src/ipa/raspberrypi/controller/rpi/awb.cpp b/src/ipa/raspberrypi/controller/rpi/awb.cpp
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--- a/src/ipa/raspberrypi/controller/rpi/awb.cpp
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-/* SPDX-License-Identifier: BSD-2-Clause */
-/*
- * Copyright (C) 2019, Raspberry Pi Ltd
- *
- * awb.cpp - AWB control algorithm
- */
-
-#include <assert.h>
-#include <functional>
-
-#include <libcamera/base/log.h>
-
-#include "../lux_status.h"
-
-#include "awb.h"
-
-using namespace RPiController;
-using namespace libcamera;
-
-LOG_DEFINE_CATEGORY(RPiAwb)
-
-#define NAME "rpi.awb"
-
-/*
- * todo - the locking in this algorithm needs some tidying up as has been done
- * elsewhere (ALSC and AGC).
- */
-
-int AwbMode::read(const libcamera::YamlObject &params)
-{
- auto value = params["lo"].get<double>();
- if (!value)
- return -EINVAL;
- ctLo = *value;
-
- value = params["hi"].get<double>();
- if (!value)
- return -EINVAL;
- ctHi = *value;
-
- return 0;
-}
-
-int AwbPrior::read(const libcamera::YamlObject &params)
-{
- auto value = params["lux"].get<double>();
- if (!value)
- return -EINVAL;
- lux = *value;
-
- return prior.read(params["prior"]);
-}
-
-static int readCtCurve(Pwl &ctR, Pwl &ctB, const libcamera::YamlObject &params)
-{
- if (params.size() % 3) {
- LOG(RPiAwb, Error) << "AwbConfig: incomplete CT curve entry";
- return -EINVAL;
- }
-
- if (params.size() < 6) {
- LOG(RPiAwb, Error) << "AwbConfig: insufficient points in CT curve";
- return -EINVAL;
- }
-
- const auto &list = params.asList();
-
- for (auto it = list.begin(); it != list.end(); it++) {
- auto value = it->get<double>();
- if (!value)
- return -EINVAL;
- double ct = *value;
-
- assert(it == list.begin() || ct != ctR.domain().end);
-
- value = (++it)->get<double>();
- if (!value)
- return -EINVAL;
- ctR.append(ct, *value);
-
- value = (++it)->get<double>();
- if (!value)
- return -EINVAL;
- ctB.append(ct, *value);
- }
-
- return 0;
-}
-
-int AwbConfig::read(const libcamera::YamlObject &params)
-{
- int ret;
-
- bayes = params["bayes"].get<int>(1);
- framePeriod = params["frame_period"].get<uint16_t>(10);
- startupFrames = params["startup_frames"].get<uint16_t>(10);
- convergenceFrames = params["convergence_frames"].get<unsigned int>(3);
- speed = params["speed"].get<double>(0.05);
-
- if (params.contains("ct_curve")) {
- ret = readCtCurve(ctR, ctB, params["ct_curve"]);
- if (ret)
- return ret;
- /* We will want the inverse functions of these too. */
- ctRInverse = ctR.inverse();
- ctBInverse = ctB.inverse();
- }
-
- if (params.contains("priors")) {
- for (const auto &p : params["priors"].asList()) {
- AwbPrior prior;
- ret = prior.read(p);
- if (ret)
- return ret;
- if (!priors.empty() && prior.lux <= priors.back().lux) {
- LOG(RPiAwb, Error) << "AwbConfig: Prior must be ordered in increasing lux value";
- return -EINVAL;
- }
- priors.push_back(prior);
- }
- if (priors.empty()) {
- LOG(RPiAwb, Error) << "AwbConfig: no AWB priors configured";
- return ret;
- }
- }
- if (params.contains("modes")) {
- for (const auto &[key, value] : params["modes"].asDict()) {
- ret = modes[key].read(value);
- if (ret)
- return ret;
- if (defaultMode == nullptr)
- defaultMode = &modes[key];
- }
- if (defaultMode == nullptr) {
- LOG(RPiAwb, Error) << "AwbConfig: no AWB modes configured";
- return -EINVAL;
- }
- }
-
- minPixels = params["min_pixels"].get<double>(16.0);
- minG = params["min_G"].get<uint16_t>(32);
- minRegions = params["min_regions"].get<uint32_t>(10);
- deltaLimit = params["delta_limit"].get<double>(0.2);
- coarseStep = params["coarse_step"].get<double>(0.2);
- transversePos = params["transverse_pos"].get<double>(0.01);
- transverseNeg = params["transverse_neg"].get<double>(0.01);
- if (transversePos <= 0 || transverseNeg <= 0) {
- LOG(RPiAwb, Error) << "AwbConfig: transverse_pos/neg must be > 0";
- return -EINVAL;
- }
-
- sensitivityR = params["sensitivity_r"].get<double>(1.0);
- sensitivityB = params["sensitivity_b"].get<double>(1.0);
-
- if (bayes) {
- if (ctR.empty() || ctB.empty() || priors.empty() ||
- defaultMode == nullptr) {
- LOG(RPiAwb, Warning)
- << "Bayesian AWB mis-configured - switch to Grey method";
- bayes = false;
- }
- }
- fast = params[fast].get<int>(bayes); /* default to fast for Bayesian, otherwise slow */
- whitepointR = params["whitepoint_r"].get<double>(0.0);
- whitepointB = params["whitepoint_b"].get<double>(0.0);
- if (bayes == false)
- sensitivityR = sensitivityB = 1.0; /* nor do sensitivities make any sense */
- return 0;
-}
-
-Awb::Awb(Controller *controller)
- : AwbAlgorithm(controller)
-{
- asyncAbort_ = asyncStart_ = asyncStarted_ = asyncFinished_ = false;
- mode_ = nullptr;
- manualR_ = manualB_ = 0.0;
- asyncThread_ = std::thread(std::bind(&Awb::asyncFunc, this));
-}
-
-Awb::~Awb()
-{
- {
- std::lock_guard<std::mutex> lock(mutex_);
- asyncAbort_ = true;
- }
- asyncSignal_.notify_one();
- asyncThread_.join();
-}
-
-char const *Awb::name() const
-{
- return NAME;
-}
-
-int Awb::read(const libcamera::YamlObject &params)
-{
- return config_.read(params);
-}
-
-void Awb::initialise()
-{
- frameCount_ = framePhase_ = 0;
- /*
- * Put something sane into the status that we are filtering towards,
- * just in case the first few frames don't have anything meaningful in
- * them.
- */
- if (!config_.ctR.empty() && !config_.ctB.empty()) {
- syncResults_.temperatureK = config_.ctR.domain().clip(4000);
- syncResults_.gainR = 1.0 / config_.ctR.eval(syncResults_.temperatureK);
- syncResults_.gainG = 1.0;
- syncResults_.gainB = 1.0 / config_.ctB.eval(syncResults_.temperatureK);
- } else {
- /* random values just to stop the world blowing up */
- syncResults_.temperatureK = 4500;
- syncResults_.gainR = syncResults_.gainG = syncResults_.gainB = 1.0;
- }
- prevSyncResults_ = syncResults_;
- asyncResults_ = syncResults_;
-}
-
-void Awb::disableAuto()
-{
- /* Freeze the most recent values, and treat them as manual gains */
- manualR_ = syncResults_.gainR = prevSyncResults_.gainR;
- manualB_ = syncResults_.gainB = prevSyncResults_.gainB;
- syncResults_.gainG = prevSyncResults_.gainG;
- syncResults_.temperatureK = prevSyncResults_.temperatureK;
-}
-
-void Awb::enableAuto()
-{
- manualR_ = 0.0;
- manualB_ = 0.0;
-}
-
-unsigned int Awb::getConvergenceFrames() const
-{
- /*
- * If not in auto mode, there is no convergence
- * to happen, so no need to drop any frames - return zero.
- */
- if (!isAutoEnabled())
- return 0;
- else
- return config_.convergenceFrames;
-}
-
-void Awb::setMode(std::string const &modeName)
-{
- modeName_ = modeName;
-}
-
-void Awb::setManualGains(double manualR, double manualB)
-{
- /* If any of these are 0.0, we swich back to auto. */
- manualR_ = manualR;
- manualB_ = manualB;
- /*
- * If not in auto mode, set these values into the syncResults which
- * means that Prepare() will adopt them immediately.
- */
- if (!isAutoEnabled()) {
- syncResults_.gainR = prevSyncResults_.gainR = manualR_;
- syncResults_.gainG = prevSyncResults_.gainG = 1.0;
- syncResults_.gainB = prevSyncResults_.gainB = manualB_;
- if (config_.bayes) {
- /* Also estimate the best corresponding colour temperature from the curves. */
- double ctR = config_.ctRInverse.eval(config_.ctRInverse.domain().clip(1 / manualR_));
- double ctB = config_.ctBInverse.eval(config_.ctBInverse.domain().clip(1 / manualB_));
- prevSyncResults_.temperatureK = (ctR + ctB) / 2;
- syncResults_.temperatureK = prevSyncResults_.temperatureK;
- }
- }
-}
-
-void Awb::switchMode([[maybe_unused]] CameraMode const &cameraMode,
- Metadata *metadata)
-{
- /* Let other algorithms know the current white balance values. */
- metadata->set("awb.status", prevSyncResults_);
-}
-
-bool Awb::isAutoEnabled() const
-{
- return manualR_ == 0.0 || manualB_ == 0.0;
-}
-
-void Awb::fetchAsyncResults()
-{
- LOG(RPiAwb, Debug) << "Fetch AWB results";
- asyncFinished_ = false;
- asyncStarted_ = false;
- /*
- * It's possible manual gains could be set even while the async
- * thread was running, so only copy the results if still in auto mode.
- */
- if (isAutoEnabled())
- syncResults_ = asyncResults_;
-}
-
-void Awb::restartAsync(StatisticsPtr &stats, double lux)
-{
- LOG(RPiAwb, Debug) << "Starting AWB calculation";
- /* this makes a new reference which belongs to the asynchronous thread */
- statistics_ = stats;
- /* store the mode as it could technically change */
- auto m = config_.modes.find(modeName_);
- mode_ = m != config_.modes.end()
- ? &m->second
- : (mode_ == nullptr ? config_.defaultMode : mode_);
- lux_ = lux;
- framePhase_ = 0;
- asyncStarted_ = true;
- size_t len = modeName_.copy(asyncResults_.mode,
- sizeof(asyncResults_.mode) - 1);
- asyncResults_.mode[len] = '\0';
- {
- std::lock_guard<std::mutex> lock(mutex_);
- asyncStart_ = true;
- }
- asyncSignal_.notify_one();
-}
-
-void Awb::prepare(Metadata *imageMetadata)
-{
- if (frameCount_ < (int)config_.startupFrames)
- frameCount_++;
- double speed = frameCount_ < (int)config_.startupFrames
- ? 1.0
- : config_.speed;
- LOG(RPiAwb, Debug)
- << "frame_count " << frameCount_ << " speed " << speed;
- {
- std::unique_lock<std::mutex> lock(mutex_);
- if (asyncStarted_ && asyncFinished_)
- fetchAsyncResults();
- }
- /* Finally apply IIR filter to results and put into metadata. */
- memcpy(prevSyncResults_.mode, syncResults_.mode,
- sizeof(prevSyncResults_.mode));
- prevSyncResults_.temperatureK = speed * syncResults_.temperatureK +
- (1.0 - speed) * prevSyncResults_.temperatureK;
- prevSyncResults_.gainR = speed * syncResults_.gainR +
- (1.0 - speed) * prevSyncResults_.gainR;
- prevSyncResults_.gainG = speed * syncResults_.gainG +
- (1.0 - speed) * prevSyncResults_.gainG;
- prevSyncResults_.gainB = speed * syncResults_.gainB +
- (1.0 - speed) * prevSyncResults_.gainB;
- imageMetadata->set("awb.status", prevSyncResults_);
- LOG(RPiAwb, Debug)
- << "Using AWB gains r " << prevSyncResults_.gainR << " g "
- << prevSyncResults_.gainG << " b "
- << prevSyncResults_.gainB;
-}
-
-void Awb::process(StatisticsPtr &stats, Metadata *imageMetadata)
-{
- /* Count frames since we last poked the async thread. */
- if (framePhase_ < (int)config_.framePeriod)
- framePhase_++;
- LOG(RPiAwb, Debug) << "frame_phase " << framePhase_;
- /* We do not restart the async thread if we're not in auto mode. */
- if (isAutoEnabled() &&
- (framePhase_ >= (int)config_.framePeriod ||
- frameCount_ < (int)config_.startupFrames)) {
- /* Update any settings and any image metadata that we need. */
- struct LuxStatus luxStatus = {};
- luxStatus.lux = 400; /* in case no metadata */
- if (imageMetadata->get("lux.status", luxStatus) != 0)
- LOG(RPiAwb, Debug) << "No lux metadata found";
- LOG(RPiAwb, Debug) << "Awb lux value is " << luxStatus.lux;
-
- if (asyncStarted_ == false)
- restartAsync(stats, luxStatus.lux);
- }
-}
-
-void Awb::asyncFunc()
-{
- while (true) {
- {
- std::unique_lock<std::mutex> lock(mutex_);
- asyncSignal_.wait(lock, [&] {
- return asyncStart_ || asyncAbort_;
- });
- asyncStart_ = false;
- if (asyncAbort_)
- break;
- }
- doAwb();
- {
- std::lock_guard<std::mutex> lock(mutex_);
- asyncFinished_ = true;
- }
- syncSignal_.notify_one();
- }
-}
-
-static void generateStats(std::vector<Awb::RGB> &zones,
- RgbyRegions &stats, double minPixels,
- double minG)
-{
- for (auto const &region : stats) {
- Awb::RGB zone;
- if (region.counted >= minPixels) {
- zone.G = region.val.gSum / region.counted;
- if (zone.G >= minG) {
- zone.R = region.val.rSum / region.counted;
- zone.B = region.val.bSum / region.counted;
- zones.push_back(zone);
- }
- }
- }
-}
-
-void Awb::prepareStats()
-{
- zones_.clear();
- /*
- * LSC has already been applied to the stats in this pipeline, so stop
- * any LSC compensation. We also ignore config_.fast in this version.
- */
- generateStats(zones_, statistics_->awbRegions, config_.minPixels,
- config_.minG);
- /*
- * apply sensitivities, so values appear to come from our "canonical"
- * sensor.
- */
- for (auto &zone : zones_) {
- zone.R *= config_.sensitivityR;
- zone.B *= config_.sensitivityB;
- }
-}
-
-double Awb::computeDelta2Sum(double gainR, double gainB)
-{
- /*
- * Compute the sum of the squared colour error (non-greyness) as it
- * appears in the log likelihood equation.
- */
- double delta2Sum = 0;
- for (auto &z : zones_) {
- double deltaR = gainR * z.R - 1 - config_.whitepointR;
- double deltaB = gainB * z.B - 1 - config_.whitepointB;
- double delta2 = deltaR * deltaR + deltaB * deltaB;
- /* LOG(RPiAwb, Debug) << "deltaR " << deltaR << " deltaB " << deltaB << " delta2 " << delta2; */
- delta2 = std::min(delta2, config_.deltaLimit);
- delta2Sum += delta2;
- }
- return delta2Sum;
-}
-
-Pwl Awb::interpolatePrior()
-{
- /*
- * Interpolate the prior log likelihood function for our current lux
- * value.
- */
- if (lux_ <= config_.priors.front().lux)
- return config_.priors.front().prior;
- else if (lux_ >= config_.priors.back().lux)
- return config_.priors.back().prior;
- else {
- int idx = 0;
- /* find which two we lie between */
- while (config_.priors[idx + 1].lux < lux_)
- idx++;
- double lux0 = config_.priors[idx].lux,
- lux1 = config_.priors[idx + 1].lux;
- return Pwl::combine(config_.priors[idx].prior,
- config_.priors[idx + 1].prior,
- [&](double /*x*/, double y0, double y1) {
- return y0 + (y1 - y0) *
- (lux_ - lux0) / (lux1 - lux0);
- });
- }
-}
-
-static double interpolateQuadatric(Pwl::Point const &a, Pwl::Point const &b,
- Pwl::Point const &c)
-{
- /*
- * Given 3 points on a curve, find the extremum of the function in that
- * interval by fitting a quadratic.
- */
- const double eps = 1e-3;
- Pwl::Point ca = c - a, ba = b - a;
- double denominator = 2 * (ba.y * ca.x - ca.y * ba.x);
- if (abs(denominator) > eps) {
- double numerator = ba.y * ca.x * ca.x - ca.y * ba.x * ba.x;
- double result = numerator / denominator + a.x;
- return std::max(a.x, std::min(c.x, result));
- }
- /* has degenerated to straight line segment */
- return a.y < c.y - eps ? a.x : (c.y < a.y - eps ? c.x : b.x);
-}
-
-double Awb::coarseSearch(Pwl const &prior)
-{
- points_.clear(); /* assume doesn't deallocate memory */
- size_t bestPoint = 0;
- double t = mode_->ctLo;
- int spanR = 0, spanB = 0;
- /* Step down the CT curve evaluating log likelihood. */
- while (true) {
- double r = config_.ctR.eval(t, &spanR);
- double b = config_.ctB.eval(t, &spanB);
- double gainR = 1 / r, gainB = 1 / b;
- double delta2Sum = computeDelta2Sum(gainR, gainB);
- double priorLogLikelihood = prior.eval(prior.domain().clip(t));
- double finalLogLikelihood = delta2Sum - priorLogLikelihood;
- LOG(RPiAwb, Debug)
- << "t: " << t << " gain R " << gainR << " gain B "
- << gainB << " delta2_sum " << delta2Sum
- << " prior " << priorLogLikelihood << " final "
- << finalLogLikelihood;
- points_.push_back(Pwl::Point(t, finalLogLikelihood));
- if (points_.back().y < points_[bestPoint].y)
- bestPoint = points_.size() - 1;
- if (t == mode_->ctHi)
- break;
- /* for even steps along the r/b curve scale them by the current t */
- t = std::min(t + t / 10 * config_.coarseStep, mode_->ctHi);
- }
- t = points_[bestPoint].x;
- LOG(RPiAwb, Debug) << "Coarse search found CT " << t;
- /*
- * We have the best point of the search, but refine it with a quadratic
- * interpolation around its neighbours.
- */
- if (points_.size() > 2) {
- unsigned long bp = std::min(bestPoint, points_.size() - 2);
- bestPoint = std::max(1UL, bp);
- t = interpolateQuadatric(points_[bestPoint - 1],
- points_[bestPoint],
- points_[bestPoint + 1]);
- LOG(RPiAwb, Debug)
- << "After quadratic refinement, coarse search has CT "
- << t;
- }
- return t;
-}
-
-void Awb::fineSearch(double &t, double &r, double &b, Pwl const &prior)
-{
- int spanR = -1, spanB = -1;
- config_.ctR.eval(t, &spanR);
- config_.ctB.eval(t, &spanB);
- double step = t / 10 * config_.coarseStep * 0.1;
- int nsteps = 5;
- double rDiff = config_.ctR.eval(t + nsteps * step, &spanR) -
- config_.ctR.eval(t - nsteps * step, &spanR);
- double bDiff = config_.ctB.eval(t + nsteps * step, &spanB) -
- config_.ctB.eval(t - nsteps * step, &spanB);
- Pwl::Point transverse(bDiff, -rDiff);
- if (transverse.len2() < 1e-6)
- return;
- /*
- * unit vector orthogonal to the b vs. r function (pointing outwards
- * with r and b increasing)
- */
- transverse = transverse / transverse.len();
- double bestLogLikelihood = 0, bestT = 0, bestR = 0, bestB = 0;
- double transverseRange = config_.transverseNeg + config_.transversePos;
- const int maxNumDeltas = 12;
- /* a transverse step approximately every 0.01 r/b units */
- int numDeltas = floor(transverseRange * 100 + 0.5) + 1;
- numDeltas = numDeltas < 3 ? 3 : (numDeltas > maxNumDeltas ? maxNumDeltas : numDeltas);
- /*
- * Step down CT curve. March a bit further if the transverse range is
- * large.
- */
- nsteps += numDeltas;
- for (int i = -nsteps; i <= nsteps; i++) {
- double tTest = t + i * step;
- double priorLogLikelihood =
- prior.eval(prior.domain().clip(tTest));
- double rCurve = config_.ctR.eval(tTest, &spanR);
- double bCurve = config_.ctB.eval(tTest, &spanB);
- /* x will be distance off the curve, y the log likelihood there */
- Pwl::Point points[maxNumDeltas];
- int bestPoint = 0;
- /* Take some measurements transversely *off* the CT curve. */
- for (int j = 0; j < numDeltas; j++) {
- points[j].x = -config_.transverseNeg +
- (transverseRange * j) / (numDeltas - 1);
- Pwl::Point rbTest = Pwl::Point(rCurve, bCurve) +
- transverse * points[j].x;
- double rTest = rbTest.x, bTest = rbTest.y;
- double gainR = 1 / rTest, gainB = 1 / bTest;
- double delta2Sum = computeDelta2Sum(gainR, gainB);
- points[j].y = delta2Sum - priorLogLikelihood;
- LOG(RPiAwb, Debug)
- << "At t " << tTest << " r " << rTest << " b "
- << bTest << ": " << points[j].y;
- if (points[j].y < points[bestPoint].y)
- bestPoint = j;
- }
- /*
- * We have NUM_DELTAS points transversely across the CT curve,
- * now let's do a quadratic interpolation for the best result.
- */
- bestPoint = std::max(1, std::min(bestPoint, numDeltas - 2));
- Pwl::Point rbTest = Pwl::Point(rCurve, bCurve) +
- transverse * interpolateQuadatric(points[bestPoint - 1],
- points[bestPoint],
- points[bestPoint + 1]);
- double rTest = rbTest.x, bTest = rbTest.y;
- double gainR = 1 / rTest, gainB = 1 / bTest;
- double delta2Sum = computeDelta2Sum(gainR, gainB);
- double finalLogLikelihood = delta2Sum - priorLogLikelihood;
- LOG(RPiAwb, Debug)
- << "Finally "
- << tTest << " r " << rTest << " b " << bTest << ": "
- << finalLogLikelihood
- << (finalLogLikelihood < bestLogLikelihood ? " BEST" : "");
- if (bestT == 0 || finalLogLikelihood < bestLogLikelihood)
- bestLogLikelihood = finalLogLikelihood,
- bestT = tTest, bestR = rTest, bestB = bTest;
- }
- t = bestT, r = bestR, b = bestB;
- LOG(RPiAwb, Debug)
- << "Fine search found t " << t << " r " << r << " b " << b;
-}
-
-void Awb::awbBayes()
-{
- /*
- * May as well divide out G to save computeDelta2Sum from doing it over
- * and over.
- */
- for (auto &z : zones_)
- z.R = z.R / (z.G + 1), z.B = z.B / (z.G + 1);
- /*
- * Get the current prior, and scale according to how many zones are
- * valid... not entirely sure about this.
- */
- Pwl prior = interpolatePrior();
- prior *= zones_.size() / (double)(statistics_->awbRegions.numRegions());
- prior.map([](double x, double y) {
- LOG(RPiAwb, Debug) << "(" << x << "," << y << ")";
- });
- double t = coarseSearch(prior);
- double r = config_.ctR.eval(t);
- double b = config_.ctB.eval(t);
- LOG(RPiAwb, Debug)
- << "After coarse search: r " << r << " b " << b << " (gains r "
- << 1 / r << " b " << 1 / b << ")";
- /*
- * Not entirely sure how to handle the fine search yet. Mostly the
- * estimated CT is already good enough, but the fine search allows us to
- * wander transverely off the CT curve. Under some illuminants, where
- * there may be more or less green light, this may prove beneficial,
- * though I probably need more real datasets before deciding exactly how
- * this should be controlled and tuned.
- */
- fineSearch(t, r, b, prior);
- LOG(RPiAwb, Debug)
- << "After fine search: r " << r << " b " << b << " (gains r "
- << 1 / r << " b " << 1 / b << ")";
- /*
- * Write results out for the main thread to pick up. Remember to adjust
- * the gains from the ones that the "canonical sensor" would require to
- * the ones needed by *this* sensor.
- */
- asyncResults_.temperatureK = t;
- asyncResults_.gainR = 1.0 / r * config_.sensitivityR;
- asyncResults_.gainG = 1.0;
- asyncResults_.gainB = 1.0 / b * config_.sensitivityB;
-}
-
-void Awb::awbGrey()
-{
- LOG(RPiAwb, Debug) << "Grey world AWB";
- /*
- * Make a separate list of the derivatives for each of red and blue, so
- * that we can sort them to exclude the extreme gains. We could
- * consider some variations, such as normalising all the zones first, or
- * doing an L2 average etc.
- */
- std::vector<RGB> &derivsR(zones_);
- std::vector<RGB> derivsB(derivsR);
- std::sort(derivsR.begin(), derivsR.end(),
- [](RGB const &a, RGB const &b) {
- return a.G * b.R < b.G * a.R;
- });
- std::sort(derivsB.begin(), derivsB.end(),
- [](RGB const &a, RGB const &b) {
- return a.G * b.B < b.G * a.B;
- });
- /* Average the middle half of the values. */
- int discard = derivsR.size() / 4;
- RGB sumR(0, 0, 0), sumB(0, 0, 0);
- for (auto ri = derivsR.begin() + discard,
- bi = derivsB.begin() + discard;
- ri != derivsR.end() - discard; ri++, bi++)
- sumR += *ri, sumB += *bi;
- double gainR = sumR.G / (sumR.R + 1),
- gainB = sumB.G / (sumB.B + 1);
- asyncResults_.temperatureK = 4500; /* don't know what it is */
- asyncResults_.gainR = gainR;
- asyncResults_.gainG = 1.0;
- asyncResults_.gainB = gainB;
-}
-
-void Awb::doAwb()
-{
- prepareStats();
- LOG(RPiAwb, Debug) << "Valid zones: " << zones_.size();
- if (zones_.size() > config_.minRegions) {
- if (config_.bayes)
- awbBayes();
- else
- awbGrey();
- LOG(RPiAwb, Debug)
- << "CT found is "
- << asyncResults_.temperatureK
- << " with gains r " << asyncResults_.gainR
- << " and b " << asyncResults_.gainB;
- }
- /*
- * we're done with these; we may as well relinquish our hold on the
- * pointer.
- */
- statistics_.reset();
-}
-
-/* Register algorithm with the system. */
-static Algorithm *create(Controller *controller)
-{
- return (Algorithm *)new Awb(controller);
-}
-static RegisterAlgorithm reg(NAME, &create);
generated by cgit v1.2.1 (git 2.18.0) at 2024-11-19 05:51:49 +0000